Background: International migration is influenced by economic and social factors that change over time. However, given the complexity of these relationships, global population scenarios to date include only stylized migration assumptions that do not account for changes in the drivers of migration. On the other hand, existing projection models of international migration do not resolve all demographic dimensions necessary to interact with the cohort-component models typically used for population projections. Objective: Here we present a global model of bilateral migration that resolves these dimensions while also accounting for important external, economic, and social factors. Methods: We include age, education, and gender dependencies into a recently developed model of migration by origin, destination, and country of birth. We calibrate the model on bilateral flow data, couple it to a widely used cohort-component population model, and project migration until 2050 under three alternative socioeconomic scenarios. Conclusions: The extended model fits data better than the original migration model and is more sensitive to the choice of socioeconomic scenario, thus yielding a wider range of projections. Regional net migration flows projected by the model are substantially larger than in the stylized assumptions. The largest flows are projected in the most economically unequal scenario, while previously, the same scenario was assumed to have the smallest flows. Contribution: The results offer an opportunity to reconcile stylized migration assumptions with quantitative estimates of the roles of important migration drivers. The coupled migration-population modeling framework means that interactions between migration and other demographic processes can be captured, and the migration component can be evaluated in more detail than before.

中文翻译：

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用于队列组成人口预测的多维全球迁移模型（作者：Lucas Kluge、Orlando Olaya-Bucaro、Samir KC、Dilek Yildiz、Guy Abel、Jacob Schewe）


背景：国际移民受到随时间变化的经济和社会因素的影响。然而，鉴于这些关系的复杂性，迄今为止的全球人口情景仅包括程式化的移民假设，并未考虑移民驱动因素的变化。另一方面，现有的国际移民预测模型并不能解决与通常用于人口预测的队列组成模型相互作用所需的所有人口维度。目标：在这里，我们提出了一个双边移民的全球模型，该模型解决了这些问题，同时也考虑了重要的外部、经济和社会因素。方法：我们将年龄、教育和性别依赖性纳入最近开发的按原籍国、目的地和出生国划分的移民模型中。我们根据双边流动数据校准模型，将其与广泛使用的队列组成人口模型结合起来，并在三种替代社会经济情景下预测 2050 年之前的移民情况。结论：扩展模型比原始迁移模型更适合数据，并且对社会经济情景的选择更敏感，从而产生更广泛的预测。该模型预测的区域净移民流量远大于程式化假设。最大的流量预计出现在经济最不平等的情景中，而以前，相同的情景被假定为具有最小的流量。贡献：研究结果提供了一个机会，可以将程式化的移民假设与重要移民驱动因素的作用的定量估计相协调。 耦合的移民-人口建模框架意味着可以捕获移民与其他人口统计过程之间的相互作用，并且可以比以前更详细地评估移民组成部分。